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PATTERN RECOGNITION in SPEECH and LANGUAGE PROCESSING 3 Preface 5 Contributors 7 Contents 9 1232-ch03.pdf -1 PATTERN RECOGNITION in SPEECH and LANGUAGE PROCESSING 95 Table of Contents -1 Chapter 3. A Decision Theoretic Formulation for Robust Automatic Speech Recognition 95 Introduction 95 Optimal Bayes’ Decision Rule for ASR 97 Adaptive Decision Rules Constructed from Training Samples 99 Plug-in Bayes’ Decision Rules with Maximum-likelihood Density Estimate 100 What are Plug-in Bayes’ Decision Rules? 100 Why Could Plug-in Bayes’ Decision Rules Work? 101 Implications on Parametric Models and Parameter Estimation 101 Maximum-Discriminant Decision Rules Minimizing the Empirical Classi . cation Error 103 What are Maximum-Discriminant Decision Rules? 103 Why Could Discriminant ApproachWork? 103 Implications on the Choice of Discriminant Functions and the Practical Training Algorithms 104 Discussion 104 Violations of Modeling Assumptions in ASR 105 Types of Distortions 105 Towards Adaptive and Robust ASR 106 Improving Adaptive Decision Rules via Decision Parameter Adaptation 107 Decision Parameter Adaptation for Stationary Operating Conditions 107 Adaptation for Plug-in Decision Rules 10
- Author
- WU CHOU and BIING HWANG JUANG
- Published
- 2003
- Language
- EN
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